Update app.py

app.py

@@ -1,155 +1,8 @@
 import gradio as gr
-import cv2
-from deepface import DeepFace
-from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
-import tempfile
-
-analyzer = SentimentIntensityAnalyzer()
-
-def analyze_text(text):
-    score = analyzer.polarity_scores(text)
-    if score['compound'] >= 0.05:
-        return "Positive 😊"
-    elif score['compound'] <= -0.05:
-        return "Negative 😠"
-    else:
-        return "Neutral 😐"
-
-def process_all(text, video):
-    text_sentiment = analyze_sentiment(text)
-    video_emotion = analyze_video_emotion(video)
-    return f"Text Sentiment: {text_sentiment}\nFacial Emotion: {video_emotion}"
-
-iface = gr.Interface(
-    fn=process_all,
-    inputs=[gr.Textbox(label="Social Media Post"), gr.Video(label="Upload Video")],
-    outputs="text",
-    title="Emotion & Sentiment Analyzer"
-)
-
-iface.launch()
-
-def analyze_video(video_file):
-    if video_file is None:
-        return "No video uploaded"
-
-    # Save uploaded file temporarily
-    temp_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
-    with open(temp_path, "wb") as f:
-        f.write(video_file.read())
-
-    cap = cv2.VideoCapture(temp_path)
-    success, frame = cap.read()
-    cap.release()
-    
-def analyze_video_emotion(video_file):
-    # Save the uploaded video to a temp file
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmp:
-        tmp.write(video_file.read())
-        tmp_path = tmp.name
-
-    cap = cv2.VideoCapture(tmp_path)
-    emotions = []
-    frame_count = 0
-
-    import cv2
-import tempfile
 from deepface import DeepFace
-
-def analyze_video_emotion(video_file):
-    # Save the uploaded video to a temp file
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmp:
-        tmp.write(video_file.read())
-        tmp_path = tmp.name
-
-    cap = cv2.VideoCapture(tmp_path)
-    emotions = []
-    frame_count = 0
-
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret or frame_count > 60:  # Limit to first 60 frames
-            break
-        try:
-            result = DeepFace.analyze(frame, actions=['emotion'], enforce_detection=False)
-            emotions.append(result[0]['dominant_emotion'])
-        except Exception as e:
-            print("Error analyzing frame:", e)
-        frame_count += 1
-
-    cap.release()
-
-    if emotions:
-        # Return most frequent emotion
-        return max(set(emotions), key=emotions.count)
-    else:
-        return "No emotion detected or face not found"
-
-
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret or frame_count > 60:  # Limit to 60 frames max
-            break
-        try:
-            result = DeepFace.analyze(frame, actions=['emotion'], enforce_detection=False)
-            emotions.append(result[0]['dominant_emotion'])
-        except:
-            pass
-        frame_count += 1
-
-    cap.release()
-
-    if emotions:
-        # Return most common emotion
-        return max(set(emotions), key=emotions.count)
-    else:
-        return "No face detected"
-
-
-    if not success:
-        return "Could not read video"
-
-    try:
-        result = DeepFace.analyze(frame, actions=["emotion"], enforce_detection=False)
-        return result[0]['dominant_emotion'].capitalize()
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-def analyze_post(text, video):
-    sentiment = analyze_text(text)
-    emotion = analyze_video(video)
-    return f"📝 Sentiment: {sentiment}\n🎥 Emotion: {emotion}"
-import gradio as gr
-
-def analyze_text(text):
-    from transformers import pipeline
-    classifier = pipeline("sentiment-analysis")
-    return classifier(text)[0]['label']
-
-def process_all(text_input, video_input):
-    text_result = analyze_text(text_input)
-    video_result = analyze_video_emotion(video_input)
-    return f"Text Sentiment: {text_result}\nFacial Emotion: {video_result}"
-
-gr.Interface(
-    fn=process_all,
-    inputs=[
-        gr.Textbox(label="Enter Social Media Text"),
-        gr.Video(label="Upload a Video Clip")
-    ],
-    outputs="text",
-    title="Emotion & Sentiment Decoder",
-    description="Analyzes social media text & facial expressions from video."
-).launch()
-    
-
-interface = gr.Interface(
-    fn=analyze_post,
-    inputs=[
-        gr.Textbox(label="Post Text", placeholder="Enter your message here"),
-        gr.File(label="Upload video (.mp4)", file_types=[".mp4"])
-    ],
 from transformers import pipeline
+import tempfile
+import cv2
 import moviepy.editor as mp
 
 def analyze_text(text):
@@ -204,9 +57,11 @@ iface = gr.Interface(
 
 iface.launch()
 
-    outputs="text",
-    title="📱 Emotion & Sentiment Analyzer",
-    description="Analyze text sentiment and facial emotion from video. No re-running needed. Permanent on Hugging Face."
-)
 
-interface.launch()
+
+
+       
+        
+
+
+